This invention relates to fatigue monitoring systems and methods and in particular, but not exclusively, to such systems and methods for monitoring fatigue consumption and significant structural events on board an aircraft.
It is extremely important to monitor the fatigue life of an aircraft so that it is reassessed or refurbished before the end of the fatigue life.
In one proposal, the fatigue life of an aircraft is measured by monitoring the stress at a multiplicity of locations across the aircraft, defined by a preset template. Range-mean-pairs are determined for the stresses experienced at each location and these are then used to determine from a frequency-of-occurrence matrix whether the aircraft has exceeded its design limits at any one of the locations. By monitoring the fatigue of an aircraft in this way, maintenance actions may be planned effectively, and the fatigue life of a fleet of aircraft may be managed pro-actively, by rotation of the aircraft. The fatigue life and stress spectra of a structure may be monitored at a location either directly, by a suitably calibrated strain gauge or they may be detected indirectly. In this latter method, data is taken from a flight control system relating to the manoeuvres the aircraft has gone through or is going through, from which G-forces, stresses and strains may be calculated.
We have found that there is a problem with both the direct and indirect methods insofar as the data they provide is often corrupted and can give spurious false readings. There may also be calibration problems such that, for example, the air speed readings upon which stresses and strains are calculated may be in error. In a conventional solution, the data is manually analysed by ground staff to reject obviously false readings by inspection. However this is a long labourious task, and expensive for those that have to meet the costs of operating the aircraft.
Accordingly there is a need for a system and a method which reduces or obviates the need for manual analysis of the data yet which does not substantially compromise the reliability of the data.
In one aspect, this invention provides a fatigue monitoring system for monitoring the structural health of a structure, said system including means for generating a stream of data related to the stresses experienced at a plurality of locations over said structure during operation, means for supplying said stream of data to a neural network trained to remove from the data stream values deemed to be in error, and means for thereafter processing said data to determine the fatigue life of the structuring.
Preferably, the fatigue monitoring system further includes a plurality of sensors disposed at different locations in said structure for producing output signals representative of the local stress at the respective locations, said neural network further being operable to flag the identity of a defective sensor, on the basis of the erroneous data supplied thereby.
Preferably, the fatigue monitoring system includes a movement control system operable to provide data representative of the movement and acceleration of the structure, means storing a plurality of templates or models representing a series of parameter envelopes for typical operating conditions, and means for comparing the data representative of the actual stresses across the structure with a selected template and determining whether the actual stresses lie outside the parameter envelope defined by the selected template. Additionally the means for comparing the data is future operable to provide a coefficient of actual stress life.
Preferably, said neural network is trained on the basis of probability density functions.
Preferably the data supplied by said neural network is processed using a range-mean-pairs algorithm to determine said fatigue life.
In another aspect, this invention provides a fatigue monitoring method, said fatigue monitoring method comprising providing a stream of data related to the stresses experienced at a plurality of locations over the structure during operation, supplying said stream of data to a neural network trained to remove from the data stream values deemed to be in error, and thereafter determining the fatigue life of the structure from the data passed by said neural network.
Whilst the invention has been described above, it extends to any inventive combination of the features set out above or in the following description.